The present invention relates to the field of signal processing, and more particularly to methods of processing measurement signals characterized by peaks mixed with background noise.
In recent decades, techniques for chemical analysis have substantially improved because of developments in electronics and computer sciences. Several techniques can nowadays detect and quantify extremely small amounts of materials with surprising selectivity and specificity. For example, mass spectrometry is capable of detecting a single ion (atom or molecule). Thus, the analytical instrument is no longer the limiting factor in chemical analysis. The limiting factor has become the ability to extract the signal of interest from the interfering signal that can be due to the presence of other substances, electrical noise, spikes or other sources of noise involved in the analytical procedure. Although primary analytical techniques usually provide a two dimensional graph of signal intensity as a function of some variable (wavelength, mass, distance, time etc.), many hyphenated techniques have been introduced in recent years that can provide multidimensional data matrix. This is the case for techniques such as gas chromatography-mass spectrometry (GC/MS), liquid chromatography-mass spectrometry (LC/MS), pyrolysis-mass spectrometry (Py-MS) and other techniques where a separation technique or other is coupled to a spectroscopic technique. When using these instruments, a multidimensional data matrix (intensity-variable1-variable2) is obtained from which the signal of interest must be extracted.
Background signal has become an important factor in the interpretation of analytical data as instrument sensitivity is constantly increased, as discussed by Cairns et al. in Mass. Spectrom. Rev., 8, (1989), p. 93., by Tomer et al. in J.Chromatogr., 492, (1989), p.189., and by Niessen et al. in xe2x80x9cLiquid Chromatography-Mass Spectrometryxe2x80x9d, ed. by J.Cazes, Marcel Dekker Inc., New York, (1992), p.399. In hyphenated techniques such as LC/MS, GC/MS and Py-MS, background signal can mask the signals of interest. In GC/MS, the phenomenon is mainly due to the ionization of the chromatographic stationary phase. In LC/MS, since the mobile phase is overwhelmingly more concentrated than the analytes, it can create a background signal that will mask the elution peaks of interest and contaminate the mass spectra which makes interpretation very difficult. In Py-MS, a significant background signal is generated during pyrolysis that dilutes the information content of the mass spectra obtained during analysis.
FIG. 1A shows a typical LC/MS chromatogram obtained for a pharmaceutical mixture using a prior art method, in the form of the total ion current (TIC) intensity in percentage as a function of time as a first variable. FIG. 1B shows the single ion current (SIC) chromatogram obtained with the same mixture at a specific value for the mass as a second variable (mass=101). The TIC chromatogram is obtained by compressing the mass axis, intensities of all the mass peaks being added and projected on the intensity axis, as well known in the art. Each of the elution peaks 20 present in the chromatogram of FIG. 1A has a third dimension which is the mass spectrum. It can be seen from FIG. 1A that it is clearly difficult to determine the position of the elution peaks from the raw TIC chromatogram data, because of the variation and intensity of the background noise signal, a portion of which being generally designated at 22. Because the background signal is high, it is difficult to determine the true elution peaks corresponding to the compounds present in the mixture. In a similar way, it can be seen from FIG. 1B that the raw mass spectrum peak data 24 are so contaminated by background noise such as the spike appearing at time 179 and designated at 26, that it becomes very difficult to attempt identification of the compounds being present.
The need for algorithms that can remove background signal from analytical data in these techniques has been recognized for several years and many approaches have been suggested for GC/MS, ICP/MS AND LC/MS, by Lee et al. in Anal. Chem., 63, (1991), p.357., by Burton et al. in Spectrochimica Acta, Vol. 47B, 14, (1992), p. E1621 and by Hau et al. in Spectrochimica Acta, Vol, 48B, 8, (1993), p. E1047. Although some of these approaches have merit under given experimental conditions, they are generally not satisfactory for a broad scope of applications involving various experimental conditions. One of the simplest prior art approach that has been suggested and used over the years consists in subtracting the spectrum or an average of the spectra that come just before or after the elution peak from that contained under the elution peak. This approach can be efficient if the spectrum before the elution peak is representative of the background and if it is of substantially lower intensity. The elution peak has to be more intense than the background for this technique to be used. In many cases, this is not the case and erroneous results can be obtained because of over or under estimation of the background signal to be subtracted. Other approaches relying on smoothing techniques have been suggested to detect elution peaks, such by Geladi et al. in Analytica Chimica Acta, 185. (1986), p.1., by Laeven et al. in Analytica Chimica Acta, 176, (1985), p.77., by Doursma et al. in Analytica Chimica Acta, 133, (1981), p.67., by Malinowski et al. in Anal. Chem., 49, (1977), p.606., by Enke et al. in Anal. Chem., 48, (1976), p.705A., and by Lam et al in Anal. Chem., 54, (1982), p.1927. However, even if these approaches allow the determination of the elution peak, they do not remove interfering signal in the mass spectra which can lead to problems. Smoothing techniques treat the signal in the intensity-time plane but not in the mass-intensity plane An other example of smoothing approach in disclosed in U.S. Pat. No. 4,837,726 issued on Jun. 6, 1989 to Hunkapiller. Another approach as suggested by Biller et al. in Analytical Letters, 7, (1974), p.515., is based on the optimization of ion signals with time. However, this approach is only useful when the background signal is small relative to the analyte signal and it fails to recognize instrumental spikes from real elution peaks because spikes also create signal optimization with time. Lately, the technique of maximum entropy has been described in xe2x80x9cModern Spectrum Analysisxe2x80x9d, Childers, D. G. Editor, New York, IEEE Press, 1978, and by Kay at al in Proceedings of the IEEE, Vol. 69, pp 1380-1419, 1981, by Ferrige et al. in Rapid Commun. in Mass Spectrom., 5 (1991) 370, by Ferrige et al. in Rapid Commun. in Mass Spectrom., 6 (1992) 707 and by Ferrige et al. in Rapid Commun. in Mass Spectrom., 5 (1992) 765. However, this technique is lengthy and does not produce mass spectra that are stripped of the interfering ions. Even though many other prior art processing methods have been proposed, such as those described in the followings U.S. Pat. Nos. 4,314,343, 4,524,420, 4,546,643, 4,802,102, 5,291,426, 5,737,445 and 5,592,402, there is still a need for simpler methods of processing measurement signals which are effective to attenuate background noise, allowing peak detection in a broad scope of applications involving various experimental conditions.
It is therefore a object of the present invention to provide methods of processing measurement signals characterized by at least one peak mixed with a substantially regular background noise, which facilitate peak detection and interpretation of data obtained with measurement techniques such as those used in analytical experiments.
According to above object, from a broad aspect of the present invention, there is provided a method of processing data representing intensity values of a measurement signal as a function of a discrete variable, the signal being characterized by at least one peak mixed with a substantially regular background noise, the intensity values being comprised within a main intensity range. The method comprises the steps of: i) forming an intensity histogram vector representing a frequency distribution from the intensity values, the intensity histogram vector having N frequency vector components associated with corresponding N intensity sub-ranges; ii) zeroing a portion of the data corresponding to the intensity values which are below an intensity threshold value Ic derived from shape characteristics of the distribution; and iii) subtracting the determined intensity threshold value from each remaining portion of the data to obtain processed data representing the measurement signal with the peak exhibiting an enhanced signal-to-noise ratio.
From a further broad aspect of the present invention, there is provided a method of processing data representing intensity values of a measurement signal as a function of a first and a second discrete variable, the signal being characterized by at least one peak mixed with a substantially regular background noise, the intensity values as a function of the first discrete variable and associated with each one of M successive values for the second discrete variable being comprised within a corresponding main intensity range. The method comprises the steps of: i) forming M intensity histogram vectors Fj representing frequency distributions from the intensity values associated with the M successive values of the second discrete variable, each intensity histogram vector having Nj frequency vector components associated with corresponding Nj intensity sub-ranges, with j=1, . . . , M; ii) zeroing portion of the data corresponding to the intensity values associated with each distribution which are below an intensity threshold value Icj derived from shape characteristics of each distribution; and iii) subtracting the intensity threshold value from each remaining portion of the data corresponding to the intensity values associated with each distribution, to obtain processed data representing the measurement signal with the peak exhibiting an enhanced signal-to-noise ratio.